A Domain Adaptation Network Intrusion Detection Algorithm based on Class-Balanced Knowledge Transferand Multi-Structure Domain Alignment

A Domain Adaptation Network Intrusion Detection Algorithm based on Class-Balanced Knowledge Transferand Multi-Structure Domain Alignment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Domain Adaptation Network Intrusion Detection Algorithm based on Class-Balanced Knowledge Transferand Multi-Structure Domain Alignment Qian Wang, Xiang Liu, Yifan Cheng, Yongqiang Cheng, Bing Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6852869/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Network intrusion detection data has the problem of class imbalance, and the distribution of training data is inconsistent with the distribution of the real detection data, which leads to the serious performance degradation of network intrusion detection system. To solve the above problems, this paper proposes a domain adaptation network intrusion detection algorithm based on class-balanced knowledge transfer and multi-structure domain alignment. Firstly, the source domain is extended by the generative adversarial network to alleviate the data class imbalance, the class separation loss is proposed to reduce the impact of class overlap caused by data imbalance processing on cross-domain knowledge transfer, the rich knowledge of the source domain is transferred to the target domain to generate the target domain pseudo labels, and the dynamic threshold is used to lter the high condence target domain data for the subsequent domain alignment. Furthermore, multi-structure domain alignment is proposed to reduce data distribution di erences between source domain and target domain. The domain-invariant features of source domain and target domain are extracted by reducing the domain di erence caused by the dependence structure between di erent domains. The difference of local relative structure of class prototypes in di erent domains is reduced through supervised Class Prototype Discovery and class prototype relative structure alignment. Combining the domain adversarial network to align the overall distribution of data in di erent domains and the overall structure of class prototypes, the classi er can obtain more robust decision boundary. Experiments on four NIDS reference datasets, UNSWNB15, NSL-KDD,ToN-IoT, BoT-IoT, verify the e ectiveness of the proposed algorithm in the cross-domain scenarios. Network intrusion detection Adversarial domain adaptation Class imbalance Pseudo label Class prototype Copula Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 22 May, 2026 Reviews received at journal 22 May, 2026 Reviewers agreed at journal 21 May, 2026 Reviews received at journal 05 Jul, 2025 Reviewers agreed at journal 30 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers invited by journal 25 Jun, 2025 Editor assigned by journal 12 Jun, 2025 Submission checks completed at journal 12 Jun, 2025 First submitted to journal 09 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6852869","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":476096347,"identity":"d79b4ff3-512c-416c-90a6-7277a533bc14","order_by":0,"name":"Qian Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3OMQrCMBSA4ZSCLoE6xsE7BAJBKehVEgS7hCK4CApm0rGrg4cQBOdAQZeKa1YRxCFDDyBiq3tsN8H8S17gffAAcLl+NgxA8Jn8GqQt65FSqaoEH0/p3Yz7MdHRDYFpyGXzpOwki0e9DR5OqBYUgSziEsbMSqgSlEDs870WDeQtUy4RxHZyNiVZ8N26OMx7ViFakCvEKd8iRpEnK5CBNtTb4CNfZ4Z02SEiSyjspJ0IkpvHjCer6KLzedhJmpmdFDUQLJ8WA4CV32/7RX7+JoGqsOtyuVx/2QsOJ0Wcxg9PHwAAAABJRU5ErkJggg==","orcid":"","institution":"Yanshan Univer\nsity","correspondingAuthor":true,"prefix":"","firstName":"Qian","middleName":"","lastName":"Wang","suffix":""},{"id":476096348,"identity":"4e4218d9-2431-46df-8f4c-24ba5034dad1","order_by":1,"name":"Xiang Liu","email":"","orcid":"","institution":"Yanshan Univer\nsity","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Liu","suffix":""},{"id":476096349,"identity":"9c6a1b16-a710-4e6c-9403-fd95bc532bfc","order_by":2,"name":"Yifan Cheng","email":"","orcid":"","institution":"Yanshan Univer\nsity","correspondingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Cheng","suffix":""},{"id":476096350,"identity":"7c099d36-91fa-4f0e-b4f6-21b485d3a482","order_by":3,"name":"Yongqiang Cheng","email":"","orcid":"","institution":"University of Sunderland","correspondingAuthor":false,"prefix":"","firstName":"Yongqiang","middleName":"","lastName":"Cheng","suffix":""},{"id":476096351,"identity":"64a69e48-3a51-4621-9ebd-8afc97584fe4","order_by":4,"name":"Bing Zhang","email":"","orcid":"","institution":"Yanshan Univer\nsity","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-06-09 09:24:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6852869/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6852869/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85636405,"identity":"728725da-51b4-468a-9cef-fc99ff8fe8ec","added_by":"auto","created_at":"2025-06-30 06:01:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":861368,"visible":true,"origin":"","legend":"","description":"","filename":"ADomainAdaptationNetworkIntrusionDetection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6852869/v1_covered_0d187b6f-eb47-4c09-ab6b-878b2922373d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Domain Adaptation Network Intrusion Detection Algorithm based on Class-Balanced Knowledge Transferand Multi-Structure Domain Alignment","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Network intrusion detection, Adversarial domain adaptation, Class imbalance, Pseudo label, Class prototype, Copula","lastPublishedDoi":"10.21203/rs.3.rs-6852869/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6852869/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNetwork intrusion detection data has the problem of class imbalance, and the distribution of training data is inconsistent with the distribution of the real detection data, which leads to the serious performance degradation of network intrusion detection system. To solve the above problems, this paper proposes a domain adaptation network intrusion detection algorithm based on class-balanced knowledge transfer and multi-structure domain alignment. Firstly, the source domain is extended by the generative adversarial network to alleviate the data class imbalance, the class separation loss is proposed to reduce the impact of class overlap caused by data imbalance processing on cross-domain knowledge transfer, the rich knowledge of the source domain is transferred to the target domain to generate the target domain pseudo labels, and the dynamic threshold is used to lter the high condence target domain data for the subsequent domain alignment. Furthermore, multi-structure domain alignment is proposed to reduce data distribution di erences between source domain and target domain. The domain-invariant features of source domain and target domain are extracted by reducing the domain di erence caused by the dependence structure between di erent domains. 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